Yong Qian Tian

Improving understanding of dissolved organic carbon (DOC) cycling from farmlands to rivers is a challenge due to the complex influence of farming practices, the hydrology of predominantly flat lowlands, and seasonal snowpack effects. Monthly field DOC concentrations were measured throughout the year at sub-basin scale across the Chippewa River Watershed, which falls within the Corn Belt of the Midwestern United States. The observations from croplands were benchmarked against the data sampled from hilly forested areas in the Connecticut River Watershed. The Soil Water Assessment Tool (SWAT) was used to simulate daily soil water properties. This method tests for a framework for using the combination of new field data, hydrological modelling, and knowledge-based reclassification of Land Use/Land Cover (LULC) to analyze the predictors of both the spatial and temporal changes of DOC over farmlands. Our results show: 1) DOC concentrations from cropland baseflow were substantially high throughout the year, especially for spring runoff/snowmelt scenarios, 2) gradient analysis with spatial factors only was able to explain ~82% of observed annual mean DOC concentrations, and 3) with both spatial and temporal factors: [Evapotranspiration, Soil Water, and Ground Water], the analysis explained ~81% of seasonal and ~54% of daily variations in observed DOC concentrations.

Significant uncertainty exists in the estimation of dissolved organic carbon (DOC) concentration via remote sensing from colored dissolved organic matter (CDOM) absorption in inland waters pointing to a need for more process‐based understanding of the relationship between CDOM and DOC. In this study, we examine the factors affecting the covariations of DOC and CDOM using controlled experiments combined with field measurements at subbasin scale that have varying environmental and biological conditions. Our analysis reveals that the DOC:CDOM ratio is mainly related to landcover types. Higher DOC:CDOM linear regression slopes observed in evergreen leaf litter leachate suggest that CDOM comprises a smaller fraction of the DOC pool in evergreen sites in comparison to agricultural and deciduous leaf litter leachates. Given the same DOC concentrations, the range of CDOM levels from deciduous forest plant varied 3 times greater than that from other plant types. Results indicate that soil narrows the slope differences in the linear regressions of DOC from CDOM for all plant types (by 19% of evergreen, 18% of agriculture, and 77% of deciduous). Raising soil temperature by 5°C could double the range of DOC concentration and CDOM absorption for all scenarios. We present a mathematical model to estimate DOC concentration in freshwater environment via CDOM variations with reference to land cover and soil effects. The model was able to explain 95% of field measurements of multiple years in four subbasins. This improved understanding is critical for the remote sensing of DOC directly via observations of CDOM.

Remote estimation of chlorophyll-a (chl-a) in complex freshwaters remains a challenging problem due to the rapid spatial variability and wide range as influenced by terrestrial constituents. A controversial issue is whether or not 2-B models possess sufficient wavelength information for accurately estimating Chl-a concentrations from remote sensing data for freshwater environments. This study introduced a systemic approach and proved that adding additional wavelength information to 2-B model could not significantly improve the estimation of freshwater chl-a, but acted to increase model uncertainty. This convincing solution was based on a large synthetic data set (38 937 samples) combined with a set of in situ data (51 samples) collected in three cruises in Lake Huron. The synthetic data set has two distinct features: 1) large data items and 2) covers a broad range of chl-a (0-1000 mg/m ³ ), colored dissolved organic matter (CDOM) (0-50 m ^-1 ), and NAP (nonalgal particles) (0-500 mg/l). Additionally, this study reveals how hyperspectral wavelength selection, number of bands, bandwidth, and parameter calibration are associated with the uncertainty in remote sensing of chl-a. The systematic analysis approach was used to evaluate 34 chl-a algorithms by using optimal location and number of wavelengths as well as calibrated parameters. The study introduced a set of new 2-B, 3-B, and 4-B models derived also from using optimized parameters, suggested wavelengths, and bands available in MERIS and MODIS satellite images. Validation results demonstrated that these models are suitable to general freshwater environments because of broad ranges of biochemical and physical properties in both synthetic and in situ data.